AI Workslop Is a Leadership Problem, Not a Technology Problem

AI adoption is hitting turbulence. We were promised a productivity revolution. We were told that AI would handle the drudgery so we could focus on strategy. But as we look at the landscape in early 2026, a different reality has set in.

The dominant theme of the year isn't efficiency. It’s workslop: low-effort, AI-generated output that looks passable at a glance but creates more work for everyone else. It collapses under scrutiny, shifting effort downstream, eroding trust, and quietly destroying productivity. Workslop has become the primary bottleneck to real enterprise adoption.

The Internet is Filling with Slop—and Creativity is Suffering

Recent research shows that more than 20% of videos shown to new YouTube users are now AI-generated slop—low-quality, repetitive content optimized for engagement loops rather than value. Another 33% fall into what researchers label “brainrot”: content designed to loop endlessly for attention rather than provide any real substance.

Zoom out further, and the picture gets grimmer. New data suggests that over 50% of the internet is now AI-generated slop—a staggering figure that would have sounded absurd just two years ago.

Inside organizations, the pattern is repeating. A Harvard Business Review analysis described how employees are increasingly using AI tools to generate “low-effort, passable-looking work”—work that then creates a "hidden tax" for coworkers who have to fix it, contextualize it, or redo it entirely.

Microsoft’s New Future of Work report added another layer to the problem, introducing the concept of “mechanized convergence.” Teams using AI produced less diverse ideas, not more. Because models gravitate toward averages, collective output becomes repetitive and stale. Even more concerning: users with the highest confidence in AI showed a measurable decline in critical thinking.

This isn’t about bad prompts. It’s about what happens when thinking is outsourced.

The Real Cost of Workslop is Downstream

What these reports surface isn’t just a quality issue, it’s a trust and efficiency crisis. Workslop pushes the problem downstream. Instead of the creator doing the hard work upfront, the burden shifts to the receiver. According to a joint study from Stanford and BetterUp Labs:

Recipients of workslop content spent an average of two hours fixing each instance of workslop.

As the researchers put it: “The most alarming cost may be interpersonal.” 54% of employees viewed an AI-using colleague as less creative, 42% viewed them as less trustworthy, and 37% viewed them as less intelligent.

That’s the quiet danger of workslop. It doesn’t just waste time—it erodes the confidence and credibility required for high-performing teams to function.

Is Anyone Actually Surprised?

Give people a powerful tool, and many will use it to take shortcuts. I see this constantly. Teams are encouraged to use AI, and suddenly documents and decks start showing all the familiar ChatGPT "tells." Ask a couple of follow-up questions, and it becomes clear how much original thought actually went into the work.

The same dynamic is now showing up in hiring. I recently read about interviewers who’ve changed their entire process because AI now scripts so much of the resume, cover letter, and prep. One interviewer now starts every session by asking the candidate to go first with their questions.

Why? Because large language models can’t feign genuine curiosity.

Free-form discussion quickly exposes who actually understands the role and who merely outsourced their preparation. The result is deeper, richer conversations—and fewer people skating by on generated polish.

Workslop Isn’t a Technology Problem—It’s a Leadership Problem

Workslop is not an AI failure; it’s a management failure.

AI is a force multiplier. It amplifies whatever culture, incentives, and standards already exist. If speed is rewarded more than substance, AI will give you slop faster. If output matters more than ownership, you’ll get passable work that no one stands behind.

Workslop is part of the journey of this technology, but it is not the destination. Quality still wins. Thoughtful content still rises. As marketing leaders, we are still responsible for the work our teams put into the world, just as we have always been. The difference is that now, you have to be explicit about what quality work looks like.

Here are three ways to do that, drawing from the principles of being an AI Forward Marketer:

1.Be AI-Forward (Not AI-First) Being AI-forward means putting people first—your team and your customers. AI isn’t there to replace thinking; it’s there to support it. Humans must remain the editors, the owners, and the accountable parties—every time. If no one feels responsible for the output, you’ll get slop.

2.Protect Trust and Taste In an AI-saturated world, trust is your most valuable human contribution. New roles are emerging—not prompt engineers, but trust guardians. These are the people who validate, audit, and stand behind the work. Taste matters just as much; it’s what tells you when a brand voice is off or a deck lacks conviction. Google’s E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) isn't just an algorithm requirement; it's a human one.

3.Bleed a Little. Hemingway said, “There is nothing to writing. All you do is sit down at a typewriter and bleed.” That "bleeding"—the insight, the risk, the lived experience—is what gives content a soul. Strip it out, and you’re left with words that technically exist but don't actually matter.

Don't let your team settle for average. In 2026, "average" is exactly what’s breaking the internet.